基于无迹卡尔曼滤波单液流锌镍电池SOC估计  

SOC Estimation Of Single Flow Zinc Nickel Battery Based on Unscented Kalman Filter

作　　者：田曜荣 宋春宁[1] 莫伟县 TIAN Yao-rong;SONG Chun-ning;MO Wei-xian(College of Electric Engineering,Guangxi University,Nanning Guangxi 530004,China)

机构地区：[1]广西大学电气工程学院,广西南宁530004

出　　处：《计算机仿真》2021年第11期73-76,81,共5页Computer Simulation

基　　金：国家自然科学基金项目(51767005);广西自然科学基金项目(2016GXNSF AA380328)。

摘　　要：针对单液流锌镍电池荷电状态估计(SOC)还未较有为完善的解决方案,提出一种基于无迹卡尔曼滤波(UKF)算法的单液流锌镍电池SOC估计。对单液流锌镍电池工作原理进行介绍,建立单液流锌镍电池二阶等效电路模型,并对电池内部参数进行辨识,通过利用扩展卡尔曼滤波算法(EKF)和无轨迹卡尔曼滤波算法(UKF)分别对单液流锌镍电池的SOC估计,经过仿真分析两种算法的误差,进一步说明无迹卡尔曼滤波算法有较高的精确度,估计误差在2%以内,能够满足单液流锌镍电池荷电状态估计要求。Aiming at the problem that there is no perfect solution for the SOC estimation method of single flow zinc-nickel battery, a SOC estimation method based on Unscented Kalman Filter(UKF) algorithm single flow zinc-nickel battery is proposed. The working principle of single flow zinc-nickel battery was introduced, the second-order equivalent circuit model for single flow zinc-nickel battery was established and the internal parameters of the battery were identified. The SOC of single flow zinc-nickel battery was estimated by Extended Kalman Filter(EKF) algorithm and UKF algorithm. The simulation analysis of the errors of the two algorithms by experiment simulation further shows that UKF has high accuracy, the estimated error is less than 2%. It can meet the requirement of SOC estimation for a single flow zinc-nickel battery.

关 键 词：单液流锌镍电池 荷电状态估计 无迹卡尔曼 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]